Foundation Models as World Models: A Foundational Study in Text-Based GridWorlds

NeurIPSW 25 2025 62.8 method

TLDR

Evaluates foundation models as world models and agents in text-based gridworlds, showing promise for sample-efficient RL.

Reasoning

Strengths include clear empirical comparison of two strategies (FWM and FA) and demonstration that LLM improvements translate to better performance. Weaknesses are the limited scope to simple grid-world environments and lack of real-world validation.

Read-first score

Read-first score 62.8, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 43.

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=baseline,evaluation,metric,result

Recency 8%

86.7

Uses a gentle age decay so recent papers surface without erasing older foundations. 2025

Topical relevance 42%

61.4

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Field roles

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 166.

Keyword Scores

world model

model-based reinforcement learning world model

world dynamics prediction

world simulator

generative world model

interactive world model

video world model

Deep Analysis

Innovations

Proposes and evaluates two strategies for integrating foundation models into reinforcement learning: foundation world models (FWMs) that exploit prior knowledge for simulated interactions, and foundation agents (FAs) that leverage reasoning capabilities for decision-making.
Conducts a foundational empirical study in text-based grid-world environments designed to be suitable for current large language models (LLMs).

Methodology

The study uses a family of text-based grid-world environments tailored for LLMs. It empirically evaluates two approaches: foundation world models (FWMs) that use LLMs to simulate environment dynamics, and foundation agents (FAs) that use LLMs directly for decision-making. The evaluation likely compares performance across different LLM generations and against standard reinforcement learning baselines, though specific baselines and metrics are not detailed in the abstract.

Key Results

Improvements in LLMs translate into better performance for both FWMs and FAs; FAs based on current LLMs achieve excellent policies in sufficiently simple environments; the combination of FWMs with reinforcement learning agents shows high promise for more complex settings involving partial observability and stochastic elements.

Limitations

The study is limited to text-based grid-world environments, which are relatively simple and may not generalize to more complex real-world tasks.
The effectiveness of FAs is only demonstrated for sufficiently simple environments, suggesting limitations in handling more complex decision-making scenarios.
The coupling of FWMs and RL agents is described as 'highly promising' but not yet fully validated, indicating uncertainty about its practical robustness.
The results are tied to the current generation of LLMs, which may have inherent limitations in reasoning, consistency, or world knowledge.

Resources

Paper